Neural Models for Predicting Hole Diameters in Drilling Processes

Neto, Frederico de Castro; Gerônimo, Thiago Matheus; Cruz, Carlos E. D.; Aguiar, Paulo Roberto de; Bianchi, Eduardo Carlos

doi:10.1016/j.procir.2013.09.010

Cited by 16 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It must be remembered that the net topology is the selection of the number of layers and the number of nodes in the layer that determines the network capacity to learn the relationship between independent and dependent variables. At the same time, the main literature conclusions [25][26][27][28] about network topology are that a single hidden layer with few nodes is sufficient for most cases. Considering that this was the first of its kind study, a probabilistic neural network (GRN/PN) was selected to reach a precision level during the training and testing processes, as shown in Figure 2.…”

Section: Neural Nets Predictions and Software Resourcesmentioning

confidence: 99%

“…The stopping criteria were the minimum absolute number of errors obtained in most of the indoor vapour pressure predictions. In particular, the maximum absolute error allowed was fixed in 6 during the training and 9 during the testing (standard deviation ±8%), which represents an actual nearly null percentage of incorrect predictions [25][26][27][28] as a clear example of the power of ANNs to model this process. Finally, all this training process required about 1 h per office building, in a Hewlett Packard Intel i5-4200U computer.…”

Section: Ann Selection and Trainingmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A Novel Method for nZEB Internal Coverings Design Based on Neural Networks

et al. 2019

View full text Add to dashboard Cite

Research from the International Energy Agency about indoor ambiences and nearly zero energy buildings (nZEB) in the past has been centred on different aspects such as the prediction of indoor conditions as a function of the weather using laboratory material properties for simulations and real sampled data for validation. Thus, it is possible to use real data for defining behavioural groups of indoor ambiences as a function of real vapour permeability of internal coverings. However, this method is not suitable for modelling it and predicting its behaviour under weather changes, which is of interest to improve the method of selection and use of building construction materials. In this research, artificial intelligence procedures were employed as the first model of permeable coverings material behaviour to provide a newer understanding of building materials and applications for the generation of new control procedures between the mechanical and electronic point of view of building construction materials.

show abstract

Section: Neural Nets Predictions and Software Resourcesmentioning

confidence: 99%

Section: Ann Selection and Trainingmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A Novel Method for nZEB Internal Coverings Design Based on Neural Networks

et al. 2019

View full text Add to dashboard Cite

show abstract

“…A similar observation is reported by Venkata Rao et al [25] during their prediction process. Based on the literature [24][25][26][27] it is observed that the efficiency of the prediction not only varies with respect to type of algorithm, no of hidden layers, no of neurons etc but also on the no of input variables and no of output variables. Neural network module of MATLAB is explicitly used to predict responses like EWR, MER, SR and DOC based on process parameters.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A study on die sinking EDM of Nimonic C-263 super alloy : an intelligent approach to predict the process parameters using ANN

Chekuri¹,

Kalluri²,

Rajesh³

et al. 2017

IJET

View full text Add to dashboard Cite

In current study, machining characteristics of Nimonic C-263 are analysed by TAGUCHI and modelled using Artificial Neural Networks (ANN). The response parameters under consideration are Material Erosion Rate (MER), Electrode Wear Rate (EWR), Surface Roughness (SR) and Dimensional Overcut (DOC). A regression mathematical model is also developed to verify the capabilities of ANN. The modelling of ANN includes identifying appropriate combination of hidden layers and number of neurons in each hidden layer. Study on machining characteristics revealed, peak current as the most influential process parameters affecting all the responses; followed by Pulse on-time. A contrary effect is observed for Pulse off-time. A rare process parameter named flushing pressure showed negligible influence on responses. Among various ANN architectures, 6-6 architecture is noted to possess phenomenal prediction accuracy of 99.71% compared to 93.55% of regression analysis.

show abstract

“…Besides the usual MLP models, other similar approaches such as RBF-NN or hybrid approaches combining MLP with fuzzy logic or genetic algorithms have also been presented. Neto et al [13] also presented MLP and ANFIS models for the prediction of hole diameter during drilling of various alloys. For the neural network models, inputs from various sensors such as acoustic emission, electric power, force, and vibration were employed.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

et al. 2019

View full text Add to dashboard Cite

Apart from experimental research, the development of accurate and efficient models is considerably important in the field of manufacturing processes. Initially, regression models were significantly popular for this purpose, but later, the soft computing models were proven as a viable alternative to the established models. However, the effectiveness of soft computing models can be often dependent on the size of the experimental dataset, and it can be lower compared to that of the regression models for a small-sized dataset. In the present study, it is intended to conduct a comparison of the performance of various neural network models, such as the Multi-layer Perceptron (MLP), the Radial Basis Function Neural Network (RBF-NN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) models with the performance of a multiple regression model. For the development of the models, data from drilling experiments on an Al6082-T6 workpiece for various process conditions are employed, and the performance of models related to thrust force (Fz) and cutting torque (Mz) is assessed based on several criteria. From the analysis, it was found that the MLP models were superior to the other neural networks model and the regression model, as they were able to achieve a relatively lower prediction error for both models of Fz and Mz.

show abstract

Neural Models for Predicting Hole Diameters in Drilling Processes

Cited by 16 publications

References 7 publications

A Novel Method for nZEB Internal Coverings Design Based on Neural Networks

A Novel Method for nZEB Internal Coverings Design Based on Neural Networks

A study on die sinking EDM of Nimonic C-263 super alloy : an intelligent approach to predict the process parameters using ANN

A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

Contact Info

Product

Resources

About